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This paper introduces a simulation-based method for maximum likelihood estimation of stochastic Wiener systems. It is well known that the likelihood function of the observed outputs for the general class of stochastic Wiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated by...
Non-minimum phase zeros and poles of a process put upper and lower constraints on the bandwidth of a closed loop system. It is thus of great interest to be able to identify these quantities. In this contribution it is shown that non-minimum phase zeros and unstable poles can be identified using high order models without the standard o(n) (n is the model order) variance penalty for over modeling. An...
An experiment design procedure for parameter estimation in nonlinear dynamical systems is presented in this paper. The input to the system is designed in such a way that the information content of the data, as measured by a scalar function of the information matrix, is maximized. By restricting the input to a finite number of possible levels, the experiment design problem is formulated as a convex...
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